CN107465193B - Dispatching control method for alternating current-direct current power distribution network considering source storage load - Google Patents

Dispatching control method for alternating current-direct current power distribution network considering source storage load Download PDF

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CN107465193B
CN107465193B CN201710720057.2A CN201710720057A CN107465193B CN 107465193 B CN107465193 B CN 107465193B CN 201710720057 A CN201710720057 A CN 201710720057A CN 107465193 B CN107465193 B CN 107465193B
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direct current
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power distribution
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CN107465193A (en
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齐琛
李国杰
汪可友
冯琳
韩蓓
程益生
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Shanghai Jiaotong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

A dispatching control method for an alternating current-direct current power distribution network considering source storage load. The control method comprises a local scheduling layer and a regional scheduling layer: the local dispatching layer is positioned in each alternating current and direct current power distribution network region, and the regional dispatching layer is an integral alternating current and direct current hybrid power distribution network. Performing optimized dispatching on the joint output of the renewable distributed power supply and the energy storage unit and the charging load of the electric automobile at a local dispatching layer; and the regional dispatching layer dispatches the controllable distributed power generation in the power distribution network and the exchange power between the regions of the alternating current power distribution network and the direct current power distribution network by using a distributed optimization method to obtain an optimal dispatching control scheme. The dispatching control method can optimize the power supply mode of the power distribution network so as to realize the optimal utilization of the power in the alternating current and direct current power distribution network considering source storage and load.

Description

Dispatching control method for alternating current-direct current power distribution network considering source storage load
Technical Field
The invention relates to a hybrid power distribution network for AC/DC partition operation, in particular to a dispatching control method for an AC/DC power distribution network considering source storage load.
Background
The development of an active power distribution network is promoted by a new power grid technology represented by distributed power generation, and the active power distribution network with mixed alternating current and direct current is constructed by utilizing a flexible direct current technology, so that the method is a direction with a good development prospect. In a typical alternating current-direct current hybrid power distribution network, a plurality of alternating current areas are interconnected with a direct current power distribution network area through a flexible direct current converter station, and each power distribution network area runs in a cooperative mode on one hand and has strong autonomy on the other hand.
In an alternating current-direct current hybrid active power distribution network, on one hand, various active or regulation and control participating devices such as renewable energy power generation, energy storage units, electric automobile adjustable charging loads, controllable distributed power sources and the like need to be reasonably scheduled. On the other hand, the structure of the alternating current and direct current partition interconnected power grid is different from that of the traditional alternating current distribution network, and a scheduling control method suitable for the structure needs to be adopted, so that energy optimization management of the power grid is better realized.
Disclosure of Invention
The invention aims to provide a dispatching control method for an alternating current-direct current power distribution network considering source storage load. Orderly controlling the power flows of different types of distributed power supplies, energy storage and loads through hierarchical scheduling; and coordinating power exchange between the alternating current distribution network area and the direct current distribution network area through partition scheduling. And energy optimization management in the whole alternating current and direct current hybrid power distribution network is realized.
In order to achieve the above object, the technical solution of the present invention is as follows:
a method for controlling dispatching of an alternating current-direct current power distribution network considering source storage load is characterized by comprising the following steps:
1) according to the structural characteristics of an AC/DC power distribution network, a scheduling control range of the AC/DC power distribution network is divided into a local scheduling layer and a regional scheduling layer, the local scheduling layer is positioned in each AC power distribution network region or DC power distribution network region, and specific scheduling objects are the combined output of renewable energy power generation and energy storage and the charging load of an electric automobile; the region scheduling layer is an integral alternating current-direct current hybrid power distribution network, and scheduling objects are controllable distributed power supply output and exchange power between regions of the alternating current power distribution network and the direct current power distribution network;
2) initializing power data of each network node in the power distribution network according to the load prediction result;
3) respectively establishing an optimization model aiming at the combined output of renewable energy power generation and energy storage in each distribution network region, and solving, wherein the model optimization target is as follows:
Figure BDA0001384760440000011
wherein α and β are weight coefficients of the optimization objective, NTFor a set of scheduling periods, pjoi(t) and Pjoi(t) respectively the joint selling price and joint output of renewable energy and stored energy in the time period t;
4) updating power data of each node in the power distribution network according to the optimization solution result of the step 3);
5) respectively establishing an optimization model aiming at the charging load of the electric automobile in each power distribution network area, and solving, wherein the model optimization target is as follows:
Figure BDA0001384760440000021
where γ and κ are weight coefficients of the optimization objective, respectively, NTFor the set of scheduling periods, nevNumber of charging stations for electric vehicles, ρEV(t) and PEV，i(t) the charging price of the electric vehicle and the charging power of the ith charging station, P, respectively, for a time period tL(t) is the total load in the region of time period t;
6) updating power data of each node in the power distribution network according to the solving result of the step 5);
7) respectively aiming at each alternating current power distribution network region, each direct current power distribution network region and each flexible direct current converter station region, establishing an optimized scheduling model:
the dispatching target of the ith alternating current distribution network area is as follows:
Figure BDA0001384760440000022
wherein N isTFor scheduling sets of time periods, DGsACxFor a collection of controllable distributed power sources within a region,
Figure BDA0001384760440000023
and
Figure BDA0001384760440000024
the generation cost and the power rho of the jth controllable distributed power supply in the time period tthgrid,i(t) and Pgrid，i(t) cost and power, ρ, of purchasing power from a superior grid in a distribution network for a time period t, respectivelyAC,i(t) the cost of transmitting power from the ac distribution network region to the dc distribution network region for time period t,
Figure BDA0001384760440000025
and
Figure BDA0001384760440000026
the active power transmitted from the AC distribution network to the DC distribution network is calculated by the flexible DC converter station and the AC distribution network respectively in a time interval t,
Figure BDA0001384760440000027
and
Figure BDA0001384760440000028
reactive power transmitted from the alternating current distribution network to the direct current distribution network is calculated by the flexible direct current converter station and the alternating current distribution network respectively in a time period t,
Figure BDA0001384760440000029
and
Figure BDA00013847604400000210
and
Figure BDA00013847604400000211
respectively the coefficients of the penalty function,
the dispatching target of the direct current distribution network area is as follows:
Figure BDA00013847604400000212
wherein N isTFor scheduling sets of time periods, DGsDCFor a collection of controllable distributed power sources within a region,
Figure BDA00013847604400000213
and
Figure BDA00013847604400000214
the generation cost and the power of the jth controllable distributed power supply in the time interval tth are respectively, AC is a set of alternating current distribution network areas connected with the direct current distribution network areas, and rhoDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure BDA0001384760440000031
and
Figure BDA0001384760440000032
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure BDA0001384760440000033
and
Figure BDA0001384760440000034
coefficients of the penalty function, respectively;
ith Flexible direct Current converter station (VSC)i) The scheduling targets of (1) are:
Figure BDA0001384760440000035
wherein N isTFor a set of scheduling periods, pAC,i(t) cost of power transfer from AC distribution network region to DC distribution network region, ρDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure BDA0001384760440000036
and
Figure BDA0001384760440000037
the respective time interval t is the active power transmitted from the ac distribution network to the dc distribution network calculated by the flexible dc converter station and the ac distribution network,
Figure BDA0001384760440000038
and
Figure BDA0001384760440000039
the time interval t is the reactive power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the AC distribution network,
Figure BDA00013847604400000310
and
Figure BDA00013847604400000311
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure BDA00013847604400000312
and
Figure BDA00013847604400000313
and
Figure BDA00013847604400000314
and
Figure BDA00013847604400000315
coefficients of the penalty function, respectively;
8) the method comprises the following steps of solving by adopting a distributed optimization scheduling method to obtain a scheduling control result of the whole alternating current-direct current hybrid power distribution network, wherein the method comprises the following specific steps:
8.1) setting the sequence number KI of the inner loop to 0 and the sequence number KO of the outer loop to 0, and selecting the penalty function coefficient
Figure BDA00013847604400000316
Initial value and weight
Figure BDA00013847604400000317
Shared variables for initial and flexible DC converter stations
Figure BDA00013847604400000318
8.2) set KI +1, solve the optimization problem for each ac distribution network region, at this point in time
Figure BDA00013847604400000319
As a decision variable, the decision variable is,
Figure BDA00013847604400000320
simultaneously solving the optimization problem of the direct current distribution network area, at the moment
Figure BDA00013847604400000321
As a decision variable, the decision variable is,
Figure BDA00013847604400000322
obtained from the last cycle;
8.3) solving the optimization problem of the flexible direct current converter station area, at the moment
Figure BDA00013847604400000323
As a decision variable, the decision variable is,
Figure BDA00013847604400000324
obtained from step 8.2);
8.4) judging whether the inner loop converges: setting the inner-layer loop convergence judgment index as that the change of the solution result of the optimization target of two continuous inner-layer loops is less than the preset allowable range epsilon1I.e. by
||fKI-fKI-1||≤ε1(18)
Wherein f isiA vector formed by the optimization results of the ith cycle is represented; if the inner loop is converged, entering the step 8.5); otherwise, jumping back to the step 8.2);
8.5) judging whether the outer loop converges: setting the outer loop convergence judgment index as the inconsistent deviation c of the shared variables between the regionsKOLess than a predetermined allowable range epsilon2And the variation of the inconsistent deviation of the optimization sharing variables for two times is less than a preset allowable value epsilon3I.e. by
||cKO||≤ε2(19)
||cKO-cKO-1||≤ε3(20)
If the convergence is achieved, the distributed optimization of the alternating current-direct current hybrid power distribution network is integrally converged, and the optimization calculation is finished; otherwise, jumping to step 8.6);
8.6) setting KO to KO +1, and updating the coefficient vector v and the weight vector w of the augmented Lagrange penalty function by the following method:
Figure BDA0001384760440000041
Figure BDA0001384760440000042
wherein gamma is 0.25, β is more than or equal to 1;
8.7) setting
Figure BDA0001384760440000043
KI is 0, jump to step 8.2) to restart the inner loop.
The step 8) should also set the maximum inner layer circulation times KImaxAnd maximum skin circulation KOmaxIn order to prevent the occurrence of unconvergence, termination conditions are set in the inner loop and the outer loop, respectively, as follows
KI≤KImax(23)
KO≤KOmax(24)。
The invention has the beneficial effects that:
according to the invention, by utilizing layered scheduling, various types of source storage and load resources in the alternating current-direct current hybrid power distribution network can be orderly utilized, different scheduling control targets are selected aiming at different types of controllable resources, and the benefits of different participating main bodies of the power distribution network are maximized. By utilizing the partition scheduling, the structural characteristics of the alternating current-direct current hybrid power distribution network can be fully utilized, the information safety in each power distribution network region is guaranteed, and the integral optimization scheduling of the integral alternating current-direct current hybrid power distribution network is realized while the self scheduling control is independently carried out in each power distribution network region. And finally, energy optimization management of internal source load storage of the alternating current-direct current hybrid power distribution network is realized on the whole.
Drawings
Fig. 1 is a schematic diagram of a scheduling control method according to the present invention.
Fig. 2 is a flow chart of the zone scheduling layer partition scheduling control of the present invention.
Detailed Description
The present invention will be further described with reference to the following examples and drawings, but the scope of the present invention should not be limited thereto.
The invention relates to a source storage and load alternating current-direct current hybrid power distribution network optimal scheduling control method, which orderly controls the power flow of different types of distributed power supplies, stored energy and loads through layered scheduling; and power exchange between each AC and DC power distribution network region is coordinated through partition scheduling, so that energy optimization management in the whole AC/DC hybrid power distribution network is realized.
Fig. 1 is an embodiment of an ac/dc hybrid power distribution network including a distributed power supply, an energy storage, and an electric vehicle load according to the present invention, and the present invention relates to an ac/dc hybrid power distribution network optimal scheduling control method for source and storage cooperative operation, specifically including the following steps:
1) according to the structural characteristics of an AC/DC power distribution network, a dispatching control range of the AC/DC power distribution network is divided into a local dispatching layer and a regional dispatching layer, as shown in FIG. 1, the whole power distribution network is divided into three AC power distribution network regions and a DC power distribution network region, the AC/DC regions are interconnected through flexible and direct connections, the local dispatching layer is located in each AC power distribution network region and each DC power distribution network region, and specific dispatching objects are the combined output of renewable energy power generation and energy storage and the charging load of an electric vehicle; the region scheduling layer is an integral alternating current-direct current hybrid power distribution network, and scheduling objects are controllable distributed power supply output and exchange power between regions of the alternating current power distribution network and the direct current power distribution network;
2) initializing power data of each node in the power distribution network according to the load prediction result;
3) respectively establishing an optimization model aiming at the combined output of renewable energy power generation and energy storage in each power distribution network area, and solving, wherein the model optimization target is as follows:
Figure BDA0001384760440000051
subject to:
Figure BDA0001384760440000061
wherein each symbol represents the following meaning:
NT-scheduling set of time periods [0,1, 2.,. T]
Δ t-the length of the time period
α, β -respective weight coefficients for two optimization objectives
For time period t:
ρjoi(t) -Combined selling price of renewable energy and stored energy
Pjoi(t) -Combined output of renewable energy and stored energy
PRDG(t)、
Figure BDA0001384760440000062
-scheduled and predicted maximum generated power of renewable energy sources
PES,dis(t)、PES,ch(t) -discharge and charge power of energy storage device
SOCES(t) -State of Charge of energy storage device
Figure BDA0001384760440000063
Maximum discharge and charge power of the energy storage device
ηES,dis、ηES,chDischarge efficiency and charging efficiency of the energy storage device
εSOC-the allowable range of change of the state of charge of the stored energy at the end of the scheduling period with respect to the start.
Note that the optimization subject of the model is a renewable energy (and energy storage combined) power generator in a certain area, and the specification of the area number and the renewable energy power generator number is omitted in each parameter symbol of the model for the sake of simplifying the description.
4) Updating power data of each node in the power distribution network according to the optimization solution result of the step 3);
5) respectively establishing an optimization model aiming at the charging load of the electric automobile in each power distribution network area, and solving, wherein the model optimization target is as follows:
Figure BDA0001384760440000071
subject to:
Figure BDA0001384760440000072
wherein,
NT-scheduling set of time periods [0,1, 2.,. T]
Gamma, kappa-weight corresponding to each objective function
nevNumber of electric vehicle charging stations
For a period t
ρEV(t) -Charge cost of electric vehicle
PEV,i(t) -charging Power of electric vehicle charging station i
PL(t) -Total load in the region
PBL(t) -regular load in the area
Figure BDA0001384760440000073
Upper limit of maximum load of the zone
Figure BDA0001384760440000074
Integral maximum charging power of electric vehicle charging station i
Figure BDA0001384760440000075
Integral maximum and minimum accumulated charging energy of electric vehicle charging station
ηEV,iAverage charging efficiency of the electric vehicle charging station i;
6) updating power data of each node in the power distribution network according to the solving result of the step 5);
7) respectively aiming at each alternating current distribution network region, each direct current distribution network region and each flexible direct current converter station region, establishing an optimized dispatching model, and solving by adopting a distributed optimized dispatching method, wherein the flow is shown as figure 2, so as to obtain a dispatching control result of the whole alternating current-direct current hybrid distribution network,
the dispatching target of the ith alternating current distribution network area is as follows:
Figure BDA0001384760440000081
subject to:
Figure BDA0001384760440000082
wherein N isTFor scheduling sets of time periods, DGsACxFor a collection of controllable distributed power sources within a region,
Figure BDA0001384760440000083
and
Figure BDA0001384760440000084
the generation cost and the power rho of the jth controllable distributed power supply in the time period tthgrid,i(t) and Pgrid，i(t) cost and power, ρ, of purchasing power from a superior grid in a distribution network for a time period t, respectivelyAC,i(t) the cost of transmitting power from the ac distribution network region to the dc distribution network region for time period t,
Figure BDA0001384760440000085
and
Figure BDA0001384760440000086
the respective time interval t is the active power transmitted from the ac distribution network to the dc distribution network calculated by the flexible dc converter station and the ac distribution network,
Figure BDA0001384760440000087
and
Figure BDA0001384760440000088
the time interval t is the reactive power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the AC distribution network,
Figure BDA0001384760440000089
and
Figure BDA00013847604400000810
and
Figure BDA00013847604400000811
respectively, the coefficients of the penalty function.
In the constraint condition, the first two formulas represent power balance constraint; the third expression represents the upper and lower limit constraints of the node voltage; the fourth expression indicates that the power distribution network is set in the embodiment to be capable of purchasing power only from the upper-level power grid and not transmitting power to the upper-level power grid; the fifth expression and the sixth expression respectively represent the output constraint of the distributed power supply; the seventh expression and the eighth expression respectively represent the capacity constraint of the flexible direct current converter station between the alternating current power distribution network and the direct current power distribution network; the ninth expression represents a line transport capacity constraint;
the dispatching target of the direct current distribution network area is as follows:
Figure BDA00013847604400000812
subject to:
Figure BDA0001384760440000091
wherein N isTFor scheduling sets of time periods, DGsDCFor a collection of controllable distributed power sources within a region,
Figure BDA0001384760440000092
and
Figure BDA0001384760440000093
the generation cost and the power of the jth controllable distributed power supply in the time interval tth are respectively, AC is a set of alternating current distribution network areas connected with the direct current distribution network areas, and rhoDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure BDA0001384760440000094
and
Figure BDA0001384760440000095
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure BDA0001384760440000096
and
Figure BDA0001384760440000097
coefficients of the penalty function, respectively;
in the constraint condition, the first formula represents the power balance constraint in the direct current power distribution network; the second expression represents the upper and lower voltage limit constraints in the direct current distribution network; the third formula represents the output constraint of the distributed power supply; the fourth expression represents the restriction of the exchange power of the direct current power distribution network and the alternating current power distribution network, namely the active power transmission limit of the flexible direct current converter station; the final equation represents the line capacity constraints of the dc distribution network.
Ith Flexible direct Current converter station (VSC)i) The scheduling targets of (1) are:
Figure BDA0001384760440000098
subject to:
Figure BDA0001384760440000099
wherein N isTFor a set of scheduling periods, pAC,i(t) cost of power transfer from AC distribution network region to DC distribution network region, ρDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure BDA0001384760440000101
and
Figure BDA0001384760440000102
the respective time interval t is the active power transmitted from the ac distribution network to the dc distribution network calculated by the flexible dc converter station and the ac distribution network,
Figure BDA0001384760440000103
and
Figure BDA0001384760440000104
the time interval t is the reactive power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the AC distribution network,
Figure BDA0001384760440000105
and
Figure BDA0001384760440000106
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure BDA0001384760440000107
and
Figure BDA0001384760440000108
and
Figure BDA0001384760440000109
and
Figure BDA00013847604400001010
respectively, the coefficients of the penalty function.
In the constraint condition, VdcIs the voltage of the DC side of the soft DC converter station, VsThe alternating-current side voltage of the flexible direct-current converter station is calculated, delta and M are control parameters, the first formula, the second formula and the third formula are power balance constraints, the fifth formula, the sixth formula and the seventh formula are power transmission limits of the flexible direct-current converter station, and the eighth formula and the ninth formula are control parameter limits.
8) The method comprises the following steps of solving by adopting a distributed optimization scheduling method to obtain a scheduling control result of the whole alternating current-direct current hybrid power distribution network, wherein the method comprises the following specific steps:
8.1) setting the sequence number KI of the inner loop to 0 and the sequence number KO of the outer loop to 0, and selecting the penalty function coefficient
Figure BDA00013847604400001011
Initial value and weight
Figure BDA00013847604400001012
Initial value, and shared variables of flexible DC converter station
Figure BDA00013847604400001013
8.2) set KI +1, solve the optimization problem for each ac distribution network region, at this point in time
Figure BDA00013847604400001014
As a decision variable, the decision variable is,
Figure BDA00013847604400001015
simultaneously solving the optimization problem of the direct current distribution network area, at the moment
Figure BDA00013847604400001016
As a decision variable, the decision variable is,
Figure BDA00013847604400001017
obtained from the last cycle;
8.3) solving the optimization problem of the flexible direct current converter station area, at the moment
Figure BDA00013847604400001018
As a decision variable, the decision variable is,
Figure BDA00013847604400001019
obtained from step 8.2);
8.4) judging whether the inner loop converges: the inner-layer loop convergence judgment index is set as that the change of the solution result of the optimization target of two continuous inner-layer loops is smaller than a preset allowable range epsilon1I.e. by
|fKI-fKI-1|≤ε1(11)
Wherein f isiRepresenting the optimization result of the ith cycle; if the inner loop is converged, entering the step 7.5); otherwise, jumping back to the step 8.2);
8.5) judging whether the outer loop converges: the outer loop convergence judgment index is set as the inconsistent deviation c of the shared variables between the areasKILess than a predetermined allowable range epsilon2And the variation of the inconsistent deviation of the optimization sharing variables for two times is less than a preset allowable value epsilon3I.e. by
||cKO||≤ε2(12)
||cKO-cKO-1||≤ε3(13)
If the convergence is achieved, the distributed optimization of the alternating current-direct current hybrid power distribution network is integrally converged, and the optimization calculation is finished; otherwise, jumping to step 8.6);
8.6) setting KO to KO +1, and updating the coefficient vector v and the weight vector omega of the augmented Lagrange penalty function, wherein the method comprises the following steps:
ν(KO+1)＝ν(KO)+2ω(KO)·ω(KO)·c(KO)(14)
Figure BDA0001384760440000111
wherein gamma is 0.25, β is more than or equal to 1;
8.7) setting
Figure BDA0001384760440000112
KI is 0, jump to step 8.2) and repeatThe inner loop is started.
In the actual optimization process, the maximum inner-layer cycle time KI should be setmaxAnd maximum skin circulation KOmaxIn order to prevent the occurrence of unconvergence, termination conditions are set in the inner loop and the outer loop, respectively, as follows
KI≤KImax(16)
KO≤KOmax(17)
The solving flow diagram is shown in fig. 2.

Claims (2)

1. A method for controlling dispatching of an alternating current-direct current power distribution network considering source storage load is characterized by comprising the following steps:
1) according to the structural characteristics of an AC/DC power distribution network, a scheduling control range of the AC/DC power distribution network is divided into a local scheduling layer and a regional scheduling layer, the local scheduling layer is positioned in each AC power distribution network region or DC power distribution network region, and specific scheduling objects are the combined output of renewable energy power generation and energy storage and the charging load of an electric automobile; the region scheduling layer is an integral alternating current-direct current hybrid power distribution network, and scheduling objects are controllable distributed power supply output and exchange power between regions of the alternating current power distribution network and the direct current power distribution network;
2) initializing power data of each network node in the power distribution network according to the load prediction result;
3) respectively establishing an optimization model aiming at the combined output of renewable energy power generation and energy storage in each distribution network region, and solving, wherein the model optimization target is as follows:
Figure FDA0002322419020000011
wherein α and β are weight coefficients of the optimization objective, NTFor a set of scheduling periods, pjoi(t) and Pjoi(t) respectively the joint selling price and joint output of renewable energy and stored energy in the time period t;
4) updating power data of each node in the power distribution network according to the optimization solution result of the step 3);
5) respectively establishing an optimization model aiming at the charging load of the electric automobile in each power distribution network area, and solving, wherein the model optimization target is as follows:
Figure FDA0002322419020000012
where γ and κ are weight coefficients of the optimization objective, respectively, NTFor the set of scheduling periods, nevNumber of charging stations for electric vehicles, ρEV(t) and PEV，i(t) the charging price of the electric vehicle and the charging power of the ith charging station, P, respectively, for a time period tL(t) is the total load in the region of time period t;
6) updating power data of each node in the power distribution network according to the solving result of the step 5);
7) respectively aiming at each alternating current power distribution network region, each direct current power distribution network region and each flexible direct current converter station region, establishing an optimized scheduling model:
the dispatching target of the ith alternating current distribution network area is as follows:
Figure FDA0002322419020000021
wherein N isTFor scheduling sets of time periods, DGsACxFor a collection of controllable distributed power sources within a region,
Figure FDA0002322419020000022
and
Figure FDA0002322419020000023
the generation cost and the power rho of the jth controllable distributed power supply in the time period tthgrid,i(t) and Pgrid，i(t) cost and power, ρ, of purchasing power from a superior grid in a distribution network for a time period t, respectivelyAC,i(t) the cost of transmitting power from the ac distribution network region to the dc distribution network region for time period t,
Figure FDA0002322419020000024
and
Figure FDA0002322419020000025
the active power transmitted from the AC distribution network to the DC distribution network is calculated by the flexible DC converter station and the AC distribution network respectively in a time interval t,
Figure FDA0002322419020000026
and
Figure FDA0002322419020000027
reactive power transmitted from the alternating current distribution network to the direct current distribution network is calculated by the flexible direct current converter station and the alternating current distribution network respectively in a time period t,
Figure FDA0002322419020000028
and
Figure FDA0002322419020000029
and
Figure FDA00023224190200000210
respectively coefficients of an augmented lagrange penalty function,
the dispatching target of the direct current distribution network area is as follows:
Figure FDA00023224190200000211
wherein N isTFor scheduling sets of time periods, DGsDCFor a collection of controllable distributed power sources within a region,
Figure FDA00023224190200000212
and
Figure FDA00023224190200000213
the generation cost and the power of the jth controllable distributed power supply in the time interval tth are respectively, AC is a set of alternating current distribution network areas connected with the direct current distribution network areas, and rhoDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure FDA00023224190200000214
and
Figure FDA00023224190200000215
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure FDA00023224190200000216
and
Figure FDA00023224190200000217
coefficients of the augmented lagrange penalty function are respectively;
ith Flexible direct Current converter station (VSC)i) The scheduling targets of (1) are:
Figure FDA00023224190200000218
wherein N isTFor a set of scheduling periods, pAC,i(t) cost of power transfer from AC distribution network region to DC distribution network region, ρDC,i(t) the cost of purchasing power from the ith ac grid area for a time period t,
Figure FDA00023224190200000219
and
Figure FDA00023224190200000220
the respective time interval t is the active power transmitted from the ac distribution network to the dc distribution network calculated by the flexible dc converter station and the ac distribution network,
Figure FDA0002322419020000031
and
Figure FDA0002322419020000032
the time interval t is the reactive power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the AC distribution network,
Figure FDA0002322419020000033
and
Figure FDA0002322419020000034
the time interval t is respectively the active power transmitted from the AC distribution network to the DC distribution network calculated by the flexible DC converter station and the DC distribution network,
Figure FDA0002322419020000035
and
Figure FDA0002322419020000036
and
Figure FDA0002322419020000037
and
Figure FDA0002322419020000038
coefficients of the augmented lagrange penalty function are respectively;
8) the method comprises the following steps of solving by adopting a distributed optimization scheduling method to obtain a scheduling control result of the whole alternating current-direct current hybrid power distribution network, wherein the method comprises the following specific steps:
8.1) setting the sequence number KI of the inner layer to be 0 and the sequence number KO of the outer layer to be 0, and selecting the coefficient of the augmented Lagrange penalty function
Figure FDA0002322419020000039
Initial value and weight
Figure FDA00023224190200000310
Shared variables for initial and flexible DC converter stations
Figure FDA00023224190200000311
8.2) set KI +1, solve the optimization problem for each ac distribution network region, at this point in time
Figure FDA00023224190200000312
As a decision variable, the decision variable is,
Figure FDA00023224190200000313
obtaining from the last inner layer cycle, and simultaneously solving the optimization problem of the direct current distribution network area, wherein the optimization problem is obtained in the last inner layer cycle
Figure FDA00023224190200000314
As a decision variable, the decision variable is,
Figure FDA00023224190200000315
obtained from the last inner layer cycle;
8.3) solving the optimization problem of the flexible direct current converter station area, at the moment
Figure FDA00023224190200000316
As a decision variable, the decision variable is,
Figure FDA00023224190200000317
obtained from step 8.2);
8.4) judging whether the inner loop converges: setting the inner-layer loop convergence judgment index as that the change of the solution result of the optimization target of two continuous inner-layer loops is less than the preset allowable range epsilon1I.e. by
||fKI-fKI-1||≤ε1(6)
Wherein f isiA vector formed by the optimization results of the ith cycle is represented; if the inner loop is converged, entering the step 8.5); otherwise, jumping back to the step 8.2);
8.5) judging whether the outer loop converges: setting the outer loop convergence judgment index as the inconsistent deviation c of the shared variables between the regionsKOLess than a predetermined allowable range epsilon2And optimizing the variation of inconsistent deviation of shared variable twice in successionLess than a predetermined allowable value epsilon3I.e. by
||cKO||≤ε2(7)
||cKO-cKO-1||≤ε3(8)
If the convergence is achieved, the distributed optimization of the alternating current-direct current hybrid power distribution network is integrally converged, and the optimization calculation is finished; otherwise, jumping to step 8.6);
8.6) setting KO to KO +1, and updating the coefficient vector v and the weight vector w of the augmented Lagrange penalty function by the following method:
Figure FDA0002322419020000041
Figure FDA0002322419020000042
wherein gamma is 0.25, β is more than or equal to 1;
8.7) setting
Figure FDA0002322419020000043
KI is 0, jump to step 8.2) to restart the inner loop.
2. The method as claimed in claim 1, wherein the step 8) is further performed to set a maximum number KI of inner loop cyclesmaxAnd maximum skin circulation KOmaxIn order to prevent the occurrence of unconvergence, the termination conditions are set in the inner loop and the outer loop as follows:
KI≤KImax(11)
KO≤KOmax(12)。
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